11 papers · 1 filter
StyleVLA: Driving Style-Aware Vision Language Action Model for Autonomous Driving
Yuan Gao, Dengyuan Hua, Mattia Piccinini +4
Vision Language Models (VLMs) bridge visual perception and linguistic reasoning. In Autonomous Driving (AD), this synergy has enabled Vision Language Action (VLA) models, which tra…
Reinforcement Learning-based Dynamic Adaptation for Sampling-Based Motion Planning in Agile Autonomous Driving
Alexander Langmann, Yevhenii Tokarev, Mattia Piccinini +2
Sampling-based trajectory planners are widely used for agile autonomous driving due to their ability to generate fast, smooth, and kinodynamically feasible trajectories. However, t…
Real-time Velocity Profile Optimization for Time-Optimal Maneuvering with Generic Acceleration Constraints
Mattia Piazza, Mattia Piccinini, Sebastiano Taddei +2
The computation of time-optimal velocity profiles along prescribed paths, subject to generic acceleration constraints, is a crucial problem in robot trajectory planning, with parti…
Learning to Sample: Reinforcement Learning-Guided Sampling for Autonomous Vehicle Motion Planning
Korbinian Moller, Roland Stroop, Mattia Piccinini +2
Sampling-based motion planning is a well-established approach in autonomous driving, valued for its modularity and analytical tractability. In complex urban scenarios, however, uni…
Model-Structured Neural Networks to Control the Steering Dynamics of Autonomous Race Cars
Mattia Piccinini, Aniello Mungiello, Georg Jank +3
Autonomous racing has gained increasing attention in recent years, as a safe environment to accelerate the development of motion planning and control methods for autonomous driving…
MP-RBFN: Learning-based Vehicle Motion Primitives using Radial Basis Function Networks
Marc Kaufeld, Mattia Piccinini, Johannes Betz
This research introduces MP-RBFN, a novel formulation leveraging Radial Basis Function Networks for efficiently learning Motion Primitives derived from optimal control problems for…